This paper proposes an approach for visualizing individuality and inter-individual variations of human brain oscillations measured as multichannel electroencephalographic (EEG) signals in a low-dimensional space based on manifold learning. Using a unified divergence measure between spectral densities termed the "beta-divergence", we introduce an appropriate dissimilarity measure between multichannel EEG signals. Then, t-distributed stochastic neighbor embedding (t-SNE; a state-of-the-art algorithm for manifold learning) together with the beta-divergence based distance was applied to resting state EEG signals recorded from 100 healthy subjects. We were able to obtain a fine low-dimensional visualization that enabled each subject to be identified as an isolated point cloud and that represented inter-individual variations as the relationships between such point clouds. Furthermore, we also discuss how the performance of the low-dimensional visualization depends on the beta-divergence parameter and the t-SNE hyper parameter. Finally, borrowing from the concept of locally linear embedding (LLE), we propose a method for projecting the test sample to the t-SNE space obtained from the training samples and investigate that availability.

中文翻译：

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一种通过β-散度映射人脑振荡个性的多种学习方法

本文提出了一种基于流形学习的方法，用于可视化在低维空间中作为多通道脑电图 (EEG) 信号测量的人脑振荡的个性和个体间变化。使用称为“β-发散”的光谱密度之间的统一发散度量，我们在多通道 EEG 信号之间引入了适当的相异度量。然后，将 t 分布随机邻居嵌入（t-SNE；流形学习的最先进算法）与基于 β 散度的距离一起应用于从 100 名健康受试者记录的静息状态 EEG 信号。我们能够获得精细的低维可视化，使每个主题都能够被识别为一个孤立的点云，并将个体间的变化表示为这些点云之间的关系。此外，我们还讨论了低维可视化的性能如何取决于 beta-divergence 参数和 t-SNE 超参数。最后，借鉴局部线性嵌入 (LLE) 的概念，我们提出了一种将测试样本投影到从训练样本获得的 t-SNE 空间的方法，并研究其可用性。